Many computerized applications such as encryption algorithms, scientific models, and analytical tools require random numbers. A traditional approach for generating random numbers involves using a software algorithm that is provided a seed input and generates so called “random” numbers by manipulating the seed input. The use of a non-random seed injects a starting bias into the algorithm. The complex processing used to manipulate the seed also imparts a bias, and the result is a number that is not truly random. The name given to the biased output of a random number generating software algorithm is “pseudo random number”, hence these algorithms are more properly referred to as pseudo random number generators (PRNGs).
Since a pseudo random number has a bias, predicting its value, and the value of any quantity derived from it, is much easier than predicting the value of a truly random number. When pseudo random numbers are used to create encryption keys for computerized communications, the resulting key is easier to crack than a key generated from a truly random source. As advances continue to be made in mathematics and quantum computing, PRNGs are expected to become obsolete for many computer security applications. Additionally, when pseudo random numbers are used to validate scientific experiments, their bias can lead to errors.
Because of the shortcomings associated with PRNGs, it is preferable to use truly random numbers for applications requiring random numbers. A truly random number is one in which the present value is not dependent upon, nor related to, the value of any other number in the sequence; that is, the present value of the number is determined only by unbiased chance. If the value of the number is unbiased, then the likelihood that a particular number will appear will not change over time; in other words, it will always be unpredictable. Truly random numbers can be generated by sampling physical processes having fundamentally probabilistic behavior. Such physical processes come from a limited number of physical activities, or phenomena. Quantum entropy sources are desirable for random number generation, because unlike classical physics, quantum physics is fundamentally random. However, access to quantum entropy sources is currently limited. Further, single sources of entropy with a singular access channel can be vulnerable to exploitation, modification, and/or compromise of the output integrity.
There is therefore a need for systems, devices, and methods to provide widespread access to random number sequences generated based on probabilistic physical processes such as quantum entropy.